"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 			_snake_case		(     snake_case_ ,							snake_case_ ,							snake_case_ ,							unittest.TestCase			):







				"""simple docstring"""






				a      =    StableDiffusionInpaintPipeline
				a      =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
				a      =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
				a      =    frozenset(
				    []			)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
				a      =    frozenset([]			)





				def 		_lowerCAmelCase		(   self				:				Optional[int]):



											"""simple docstring"""







											torch.manual_seed(0)
											_SCREAMING_SNAKE_CASE		:    int      	=     UNetaDConditionModel(
											    block_out_channels=(3_2, 6_4)   ,     layers_per_block=2   ,     sample_size=3_2   ,     in_channels=9   ,     out_channels=4   ,     down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")   ,     up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")   ,     cross_attention_dim=3_2   ,     attention_head_dim=(2, 4)   ,     use_linear_projection=lowerCAmelCase__   ,     )
											_SCREAMING_SNAKE_CASE		:    Union[str, Any]      	=     PNDMScheduler(skip_prk_steps=lowerCAmelCase__)
											torch.manual_seed(0)
											_SCREAMING_SNAKE_CASE		:    int      	=     AutoencoderKL(
											    block_out_channels=[3_2, 6_4]   ,     in_channels=3   ,     out_channels=3   ,     down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]   ,     up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]   ,     latent_channels=4   ,     sample_size=1_2_8   ,     )
											torch.manual_seed(0)
											_SCREAMING_SNAKE_CASE		:    int      	=     CLIPTextConfig(
											    bos_token_id=0   ,     eos_token_id=2   ,     hidden_size=3_2   ,     intermediate_size=3_7   ,     layer_norm_eps=1e-05   ,     num_attention_heads=4   ,     num_hidden_layers=5   ,     pad_token_id=1   ,     vocab_size=1_0_0_0   ,     hidden_act="""gelu"""   ,     projection_dim=5_1_2   ,     )
											_SCREAMING_SNAKE_CASE		:    Any      	=     CLIPTextModel(lowerCAmelCase__)
											_SCREAMING_SNAKE_CASE		:    List[str]      	=     CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")

											_SCREAMING_SNAKE_CASE		:    Union[str, Any]      	=     {
											    '''unet''': unet,
											    '''scheduler''': scheduler,
											    '''vae''': vae,
											    '''text_encoder''': text_encoder,
											    '''tokenizer''': tokenizer,
											    '''safety_checker''': None,
											    '''feature_extractor''': None,
											}
											return components





				def 		_lowerCAmelCase		(   self				:				List[str]   ,     _A				:				Optional[Any]   ,     _A				:				Union[str, Any]=0):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:    Optional[Any]      	=     floats_tensor((1, 3, 3_2, 3_2)   ,     rng=random.Random(lowerCAmelCase__)).to(lowerCAmelCase__)
											_SCREAMING_SNAKE_CASE		:    str      	=     image.cpu().permute(0   ,     2   ,     3   ,     1)[0]
											_SCREAMING_SNAKE_CASE		:    List[Any]      	=     Image.fromarray(np.uinta(lowerCAmelCase__)).convert("""RGB""").resize((6_4, 6_4))
											_SCREAMING_SNAKE_CASE		:    int      	=     Image.fromarray(np.uinta(image + 4)).convert("""RGB""").resize((6_4, 6_4))
											if str(lowerCAmelCase__).startswith("""mps"""):
																		_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     torch.manual_seed(lowerCAmelCase__)
											else:
																		_SCREAMING_SNAKE_CASE		:    List[str]      	=     torch.Generator(device=lowerCAmelCase__).manual_seed(lowerCAmelCase__)
											_SCREAMING_SNAKE_CASE		:    str      	=     {
											    '''prompt''': '''A painting of a squirrel eating a burger''',
											    '''image''': init_image,
											    '''mask_image''': mask_image,
											    '''generator''': generator,
											    '''num_inference_steps''': 2,
											    '''guidance_scale''': 6.0,
											    '''output_type''': '''numpy''',
											}
											return inputs





				def 		_lowerCAmelCase		(   self				:				Dict):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:    List[str]      	=     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
											_SCREAMING_SNAKE_CASE		:    Tuple      	=     self.get_dummy_components()
											_SCREAMING_SNAKE_CASE		:    Tuple      	=     StableDiffusionInpaintPipeline(**lowerCAmelCase__)
											_SCREAMING_SNAKE_CASE		:    Tuple      	=     sd_pipe.to(lowerCAmelCase__)
											sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__)

											_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     self.get_dummy_inputs(lowerCAmelCase__)
											_SCREAMING_SNAKE_CASE		:    List[str]      	=     sd_pipe(**lowerCAmelCase__).images
											_SCREAMING_SNAKE_CASE		:    int      	=     image[0, -3:, -3:, -1]

											assert image.shape == (1, 6_4, 6_4, 3)
											_SCREAMING_SNAKE_CASE		:    List[str]      	=     np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476])

											assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2





				def 		_lowerCAmelCase		(   self				:				List[str]):



											"""simple docstring"""







											super().test_inference_batch_single_identical(expected_max_diff=3e-3)




@slow
@require_torch_gpu
class 			_snake_case		(     unittest.TestCase			):







				"""simple docstring"""






				def 		_lowerCAmelCase		(   self				:				List[str]):



											"""simple docstring"""







											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()





				def 		_lowerCAmelCase		(   self				:				List[str]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:    Union[str, Any]      	=     load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/sd2-inpaint/init_image.png""")
											_SCREAMING_SNAKE_CASE		:    List[str]      	=     load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""")
											_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
											    """/yellow_cat_sitting_on_a_park_bench.npy""")

											_SCREAMING_SNAKE_CASE		:    int      	=     '''stabilityai/stable-diffusion-2-inpainting'''
											_SCREAMING_SNAKE_CASE		:    List[str]      	=     StableDiffusionInpaintPipeline.from_pretrained(lowerCAmelCase__   ,     safety_checker=lowerCAmelCase__)
											pipe.to(lowerCAmelCase__)
											pipe.set_progress_bar_config(disable=lowerCAmelCase__)
											pipe.enable_attention_slicing()

											_SCREAMING_SNAKE_CASE		:    List[str]      	=     '''Face of a yellow cat, high resolution, sitting on a park bench'''

											_SCREAMING_SNAKE_CASE		:    List[Any]      	=     torch.manual_seed(0)
											_SCREAMING_SNAKE_CASE		:    Union[str, Any]      	=     pipe(
											    prompt=lowerCAmelCase__   ,     image=lowerCAmelCase__   ,     mask_image=lowerCAmelCase__   ,     generator=lowerCAmelCase__   ,     output_type="""np"""   ,     )
											_SCREAMING_SNAKE_CASE		:    List[str]      	=     output.images[0]

											assert image.shape == (5_1_2, 5_1_2, 3)
											assert np.abs(expected_image - image).max() < 9e-3





				def 		_lowerCAmelCase		(   self				:				Dict):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:    Dict      	=     load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/sd2-inpaint/init_image.png""")
											_SCREAMING_SNAKE_CASE		:    List[str]      	=     load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""")
											_SCREAMING_SNAKE_CASE		:    List[Any]      	=     load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
											    """/yellow_cat_sitting_on_a_park_bench_fp16.npy""")

											_SCREAMING_SNAKE_CASE		:    Optional[Any]      	=     '''stabilityai/stable-diffusion-2-inpainting'''
											_SCREAMING_SNAKE_CASE		:    List[str]      	=     StableDiffusionInpaintPipeline.from_pretrained(
											    lowerCAmelCase__   ,     torch_dtype=torch.floataa   ,     safety_checker=lowerCAmelCase__   ,     )
											pipe.to(lowerCAmelCase__)
											pipe.set_progress_bar_config(disable=lowerCAmelCase__)
											pipe.enable_attention_slicing()

											_SCREAMING_SNAKE_CASE		:    Union[str, Any]      	=     '''Face of a yellow cat, high resolution, sitting on a park bench'''

											_SCREAMING_SNAKE_CASE		:    int      	=     torch.manual_seed(0)
											_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     pipe(
											    prompt=lowerCAmelCase__   ,     image=lowerCAmelCase__   ,     mask_image=lowerCAmelCase__   ,     generator=lowerCAmelCase__   ,     output_type="""np"""   ,     )
											_SCREAMING_SNAKE_CASE		:    Optional[Any]      	=     output.images[0]

											assert image.shape == (5_1_2, 5_1_2, 3)
											assert np.abs(expected_image - image).max() < 5e-1





				def 		_lowerCAmelCase		(   self				:				List[Any]):



											"""simple docstring"""







											torch.cuda.empty_cache()
											torch.cuda.reset_max_memory_allocated()
											torch.cuda.reset_peak_memory_stats()

											_SCREAMING_SNAKE_CASE		:    str      	=     load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/sd2-inpaint/init_image.png""")
											_SCREAMING_SNAKE_CASE		:    Any      	=     load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""")

											_SCREAMING_SNAKE_CASE		:    int      	=     '''stabilityai/stable-diffusion-2-inpainting'''
											_SCREAMING_SNAKE_CASE		:    str      	=     PNDMScheduler.from_pretrained(lowerCAmelCase__   ,     subfolder="""scheduler""")
											_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     StableDiffusionInpaintPipeline.from_pretrained(
											    lowerCAmelCase__   ,     safety_checker=lowerCAmelCase__   ,     scheduler=lowerCAmelCase__   ,     torch_dtype=torch.floataa   ,     )
											pipe.to(lowerCAmelCase__)
											pipe.set_progress_bar_config(disable=lowerCAmelCase__)
											pipe.enable_attention_slicing(1)
											pipe.enable_sequential_cpu_offload()

											_SCREAMING_SNAKE_CASE		:    int      	=     '''Face of a yellow cat, high resolution, sitting on a park bench'''

											_SCREAMING_SNAKE_CASE		:    Tuple      	=     torch.manual_seed(0)
											_SCREAMING_SNAKE_CASE		:    int      	=     pipe(
											    prompt=lowerCAmelCase__   ,     image=lowerCAmelCase__   ,     mask_image=lowerCAmelCase__   ,     generator=lowerCAmelCase__   ,     num_inference_steps=2   ,     output_type="""np"""   ,     )

											_SCREAMING_SNAKE_CASE		:    List[str]      	=     torch.cuda.max_memory_allocated()
											# make sure that less than 2.65 GB is allocated
											assert mem_bytes < 2.65 * 1_0**9







'''simple docstring'''



import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_  :							Optional[int]             =    logging.get_logger(__name__)
def        UpperCAmelCase (    A	:					str					):
							SCREAMING_SNAKE_CASE       :   List[str]         =      SwinConfig.from_pretrained(
							    '''microsoft/swin-tiny-patch4-window7-224'''       ,		out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']					)
							SCREAMING_SNAKE_CASE       :   List[str]         =      MaskFormerConfig(backbone_config=A					)

							SCREAMING_SNAKE_CASE       :   Optional[int]         =      '''huggingface/label-files'''
							if "ade20k-full" in model_name:
														# this should be ok
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      847
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      '''maskformer-ade20k-full-id2label.json'''
							elif "ade" in model_name:
														# this should be ok
														SCREAMING_SNAKE_CASE       :   List[str]         =      150
														SCREAMING_SNAKE_CASE       :   Any         =      '''ade20k-id2label.json'''
							elif "coco-stuff" in model_name:
														# this should be ok
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      171
														SCREAMING_SNAKE_CASE       :   List[Any]         =      '''maskformer-coco-stuff-id2label.json'''
							elif "coco" in model_name:
														# TODO
														SCREAMING_SNAKE_CASE       :   str         =      133
														SCREAMING_SNAKE_CASE       :   Any         =      '''coco-panoptic-id2label.json'''
							elif "cityscapes" in model_name:
														# this should be ok
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      19
														SCREAMING_SNAKE_CASE       :   List[Any]         =      '''cityscapes-id2label.json'''
							elif "vistas" in model_name:
														# this should be ok
														SCREAMING_SNAKE_CASE       :   Tuple         =      65
														SCREAMING_SNAKE_CASE       :   Tuple         =      '''mapillary-vistas-id2label.json'''

							SCREAMING_SNAKE_CASE       :   List[str]         =      json.load(open(hf_hub_download(A       ,		A       ,		repo_type='''dataset'''					)       ,		'''r'''					)					)
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      {int(A					): v for k, v in idalabel.items()}

							return config
def        UpperCAmelCase (    A	:					Union[str, Any]					):
							SCREAMING_SNAKE_CASE       :   Dict         =      []
							# stem
							# fmt: off
							rename_keys.append(('''backbone.patch_embed.proj.weight''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight''')					)
							rename_keys.append(('''backbone.patch_embed.proj.bias''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias''')					)
							rename_keys.append(('''backbone.patch_embed.norm.weight''', '''model.pixel_level_module.encoder.model.embeddings.norm.weight''')					)
							rename_keys.append(('''backbone.patch_embed.norm.bias''', '''model.pixel_level_module.encoder.model.embeddings.norm.bias''')					)
							# stages
							for i in range(len(config.backbone_config.depths					)					):
														for j in range(config.backbone_config.depths[i]					):
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")					)

														if i < 3:
																					rename_keys.append((F"""backbone.layers.{i}.downsample.reduction.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""")					)
																					rename_keys.append((F"""backbone.layers.{i}.downsample.norm.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""")					)
																					rename_keys.append((F"""backbone.layers.{i}.downsample.norm.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""")					)
														rename_keys.append((F"""backbone.norm{i}.weight""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""")					)
														rename_keys.append((F"""backbone.norm{i}.bias""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""")					)

							# FPN
							rename_keys.append(('''sem_seg_head.layer_4.weight''', '''model.pixel_level_module.decoder.fpn.stem.0.weight''')					)
							rename_keys.append(('''sem_seg_head.layer_4.norm.weight''', '''model.pixel_level_module.decoder.fpn.stem.1.weight''')					)
							rename_keys.append(('''sem_seg_head.layer_4.norm.bias''', '''model.pixel_level_module.decoder.fpn.stem.1.bias''')					)
							for source_index, target_index in zip(range(3       ,		0       ,		-1					)       ,		range(0       ,		3					)					):
														rename_keys.append((F"""sem_seg_head.adapter_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""")					)
														rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""")					)
														rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""")					)
														rename_keys.append((F"""sem_seg_head.layer_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""")					)
														rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""")					)
														rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""")					)
							rename_keys.append(('''sem_seg_head.mask_features.weight''', '''model.pixel_level_module.decoder.mask_projection.weight''')					)
							rename_keys.append(('''sem_seg_head.mask_features.bias''', '''model.pixel_level_module.decoder.mask_projection.bias''')					)

							# Transformer decoder
							for idx in range(config.decoder_config.decoder_layers					):
														# self-attention out projection
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""")					)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""")					)
														# cross-attention out projection
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""")					)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""")					)
														# MLP 1
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc1.weight""")					)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc1.bias""")					)
														# MLP 2
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc2.weight""")					)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc2.bias""")					)
														# layernorm 1 (self-attention layernorm)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""")					)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""")					)
														# layernorm 2 (cross-attention layernorm)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""")					)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""")					)
														# layernorm 3 (final layernorm)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""")					)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""")					)

							rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.weight''', '''model.transformer_module.decoder.layernorm.weight''')					)
							rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.bias''', '''model.transformer_module.decoder.layernorm.bias''')					)

							# heads on top
							rename_keys.append(('''sem_seg_head.predictor.query_embed.weight''', '''model.transformer_module.queries_embedder.weight''')					)

							rename_keys.append(('''sem_seg_head.predictor.input_proj.weight''', '''model.transformer_module.input_projection.weight''')					)
							rename_keys.append(('''sem_seg_head.predictor.input_proj.bias''', '''model.transformer_module.input_projection.bias''')					)

							rename_keys.append(('''sem_seg_head.predictor.class_embed.weight''', '''class_predictor.weight''')					)
							rename_keys.append(('''sem_seg_head.predictor.class_embed.bias''', '''class_predictor.bias''')					)

							for i in range(3					):
														rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", F"""mask_embedder.{i}.0.weight""")					)
														rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", F"""mask_embedder.{i}.0.bias""")					)
							# fmt: on

							return rename_keys
def        UpperCAmelCase (    A	:					Union[str, Any]       ,		A	:					Tuple       ,		A	:					int					):
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      dct.pop(A					)
							SCREAMING_SNAKE_CASE       :   List[str]         =      val
def        UpperCAmelCase (    A	:					Optional[Any]       ,		A	:					List[Any]					):
							SCREAMING_SNAKE_CASE       :   List[str]         =      [int(backbone_config.embed_dim * 2**i					) for i in range(len(backbone_config.depths					)					)]
							for i in range(len(backbone_config.depths					)					):
														SCREAMING_SNAKE_CASE       :   List[str]         =      num_features[i]
														for j in range(backbone_config.depths[i]					):
																					# fmt: off
																					# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
																					SCREAMING_SNAKE_CASE       :   List[Any]         =      state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""					)
																					SCREAMING_SNAKE_CASE       :   Any         =      state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias"""					)
																					# next, add query, keys and values (in that order) to the state dict
																					SCREAMING_SNAKE_CASE       :   int         =      in_proj_weight[:dim, :]
																					SCREAMING_SNAKE_CASE       :   Tuple         =      in_proj_bias[: dim]
																					SCREAMING_SNAKE_CASE       :   List[Any]         =      in_proj_weight[
																					    dim : dim * 2, :
																					]
																					SCREAMING_SNAKE_CASE       :   str         =      in_proj_bias[
																					    dim : dim * 2
																					]
																					SCREAMING_SNAKE_CASE       :   Any         =      in_proj_weight[
																					    -dim :, :
																					]
																					SCREAMING_SNAKE_CASE       :   str         =      in_proj_bias[-dim :]
																					# fmt: on
def        UpperCAmelCase (    A	:					Any       ,		A	:					Optional[Any]					):
							# fmt: off
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      config.decoder_config.hidden_size
							for idx in range(config.decoder_config.decoder_layers					):
														# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""					)
														SCREAMING_SNAKE_CASE       :   List[str]         =      state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"""					)
														# next, add query, keys and values (in that order) to the state dict
														SCREAMING_SNAKE_CASE       :   Any         =      in_proj_weight[: hidden_size, :]
														SCREAMING_SNAKE_CASE       :   List[Any]         =      in_proj_bias[:config.hidden_size]
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      in_proj_weight[hidden_size : hidden_size * 2, :]
														SCREAMING_SNAKE_CASE       :   int         =      in_proj_bias[hidden_size : hidden_size * 2]
														SCREAMING_SNAKE_CASE       :   Any         =      in_proj_weight[-hidden_size :, :]
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      in_proj_bias[-hidden_size :]
														# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
														SCREAMING_SNAKE_CASE       :   List[Any]         =      state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""					)
														SCREAMING_SNAKE_CASE       :   List[Any]         =      state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"""					)
														# next, add query, keys and values (in that order) to the state dict
														SCREAMING_SNAKE_CASE       :   List[Any]         =      in_proj_weight[: hidden_size, :]
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      in_proj_bias[:config.hidden_size]
														SCREAMING_SNAKE_CASE       :   Dict         =      in_proj_weight[hidden_size : hidden_size * 2, :]
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      in_proj_bias[hidden_size : hidden_size * 2]
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      in_proj_weight[-hidden_size :, :]
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      in_proj_bias[-hidden_size :]
							# fmt: on
def        UpperCAmelCase (    ):
							SCREAMING_SNAKE_CASE       :   Tuple         =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
							SCREAMING_SNAKE_CASE       :   Tuple         =      Image.open(requests.get(A       ,		stream=A					).raw					)
							return im
@torch.no_grad()
def        UpperCAmelCase (    A	:					str       ,		A	:					str       ,		A	:					str       ,		A	:					bool = False					):
							SCREAMING_SNAKE_CASE       :   int         =      get_maskformer_config(A					)

							# load original state_dict
							with open(A       ,		'''rb'''					) as f:
														SCREAMING_SNAKE_CASE       :   str         =      pickle.load(A					)
							SCREAMING_SNAKE_CASE       :   List[Any]         =      data['''model''']

							# for name, param in state_dict.items():
							#     print(name, param.shape)

							# rename keys
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      create_rename_keys(A					)
							for src, dest in rename_keys:
														rename_key(A       ,		A       ,		A					)
							read_in_swin_q_k_v(A       ,		config.backbone_config					)
							read_in_decoder_q_k_v(A       ,		A					)

							# update to torch tensors
							for key, value in state_dict.items():
														SCREAMING_SNAKE_CASE       :   List[Any]         =      torch.from_numpy(A					)

							# load 🤗 model
							SCREAMING_SNAKE_CASE       :   Any         =      MaskFormerForInstanceSegmentation(A					)
							model.eval()

							for name, param in model.named_parameters():
														print(A       ,		param.shape					)

							SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      model.load_state_dict(A       ,		strict=A					)
							assert missing_keys == [
							    "model.pixel_level_module.encoder.model.layernorm.weight",
							    "model.pixel_level_module.encoder.model.layernorm.bias",
							]
							assert len(A					) == 0, F"""Unexpected keys: {unexpected_keys}"""

							# verify results
							SCREAMING_SNAKE_CASE       :   Tuple         =      prepare_img()
							if "vistas" in model_name:
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      65
							elif "cityscapes" in model_name:
														SCREAMING_SNAKE_CASE       :   List[Any]         =      65535
							else:
														SCREAMING_SNAKE_CASE       :   List[str]         =      255
							SCREAMING_SNAKE_CASE       :   Dict         =      True if '''ade''' in model_name else False
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      MaskFormerImageProcessor(ignore_index=A       ,		reduce_labels=A					)

							SCREAMING_SNAKE_CASE       :   List[str]         =      image_processor(A       ,		return_tensors='''pt'''					)

							SCREAMING_SNAKE_CASE       :   List[Any]         =      model(**A					)

							print('''Logits:'''       ,		outputs.class_queries_logits[0, :3, :3]					)

							if model_name == "maskformer-swin-tiny-ade":
														SCREAMING_SNAKE_CASE       :   Any         =      torch.tensor(
														    [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]					)
							assert torch.allclose(outputs.class_queries_logits[0, :3, :3]       ,		A       ,		atol=1e-4					)
							print('''Looks ok!'''					)

							if pytorch_dump_folder_path is not None:
														print(F"""Saving model and image processor to {pytorch_dump_folder_path}"""					)
														Path(A					).mkdir(exist_ok=A					)
														model.save_pretrained(A					)
														image_processor.save_pretrained(A					)

							if push_to_hub:
														print('''Pushing model and image processor to the hub...'''					)
														model.push_to_hub(F"""nielsr/{model_name}"""					)
														image_processor.push_to_hub(F"""nielsr/{model_name}"""					)


if __name__ == "__main__":
							lowerCAmelCase_  :							Optional[int]             =    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--model_name',
							    default='maskformer-swin-tiny-ade',
							    type=str,
							    help=('Name of the MaskFormer model you\'d like to convert',),
							)
							parser.add_argument(
							    '--checkpoint_path',
							    default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
							    type=str,
							    help='Path to the original state dict (.pth file).',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
							)
							parser.add_argument(
							    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
							)

							lowerCAmelCase_  :							List[Any]             =    parser.parse_args()
							convert_maskformer_checkpoint(
							    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
							)




